A novel Bayesian approach to acoustic emission data analysis

被引：25
作者：
Agletdinov, E. [1 ]
Pomponi, E. [2 ]
Merson, D. [1 ]
Vinogradov, A. [1 ,3 ]
机构：
[1] Togliatti State Univ, Inst Adv Technol, Tolyatti 445667, Russia
[2] CGnal Spa, Dept Data Sci & Analyt, Via Carducci 38, I-20122 Milan, Italy
[3] Norwegian Univ Sci & Technol NTNU, Dept Engn Design & Mat, N-7491 Trondheim, Norway
来源：
ULTRASONICS | 2016年 / 72卷
关键词：
Bayesian probability; Signal processing; Random time-series; Acoustic emission; CORRODED GALVANIZED STEEL; TIME-DELAY ESTIMATION; TIN COATINGS; PATTERN-RECOGNITION; NEURAL-NETWORKS; AE SIGNALS; SCRATCH; MECHANISMS; IDENTIFICATION; DEFORMATION;
D O I：
10.1016/j.ultras.2016.07.014
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
Acoustic emission (AE) technique is a popular tool for materials characterization and non-destructive testing. Originating from the stochastic motion of defects in solids, AE is a random process by nature. The challenging problem arises whenever an attempt is made to identify specific points corresponding to the changes in the trends in the fluctuating AE time series. A general Bayesian framework is proposed for the analysis of AE time series, aiming at automated finding the breakpoints signaling a crossover in the dynamics of underlying AE sources. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码：89 / 94
页数：6
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